@commanderangle Dear commanderangle,

If you use ML in your strategy but not select that option we can't guarantee for how your strategy will be evaluated and it could be filtered out.

Regards

@multi_byte-wildebeest Hi. Without an example, it's unclear what the problem might be.

If you use a state and a function that returns the prediction for one day, you will not get correct results with precheck.

This was discussed here: https://quantiacs.com/community/topic/555/access-previous-weights/18

@gjhernandezp Hi,
sorry for delay, you can check this topic:
https://quantiacs.com/community/topic/564/collections-has-no-attribute-iterable

@vg2001 Hi, the limit refers to point-in-time evaluation, namely 10 minutes per point in time, where points in time are the processed historical days.

@vg2001 Sorry for the delay. It is a bit different, we reserve the possibility to scale down volatility to 5%. This can happen if the algorithm (for example) concentrates allocations in a few assets and becomes inherently very risky to be traded.

@illustrious-felice Hi, sorry for the delay, we extended the deadline.

As far as looking forward is going on, this does not take place during the real-time simulation, as the code cannot access by construction the new data. This is true even if the code is written in a single-pass way.

If the user uses a single-pass implementation, however, looking forward in the in-sample period is possible of course, there it is responsibility of the user to prevent it. Single-pass has access to all the time series.

@magenta-kabuto Hi, did you try it? The slice function is designed to return all weights.

@multi_byte-wildebeest Hi, these limitations refer to the processing time per point in time, not for the full strategy.

If it takes 10 minutes per historical day, and the simulation has to take into account 250 days for let us say 10 years, the multi-pass simulation would process 6 days per hour, 144 days per real day, that means 2 weeks of processing time for the full submission, it is a lot of time.

@support

Thank you, I noticed some changes on the rules so I have to reimplement the strategy anyway.

@newbiequant9696 Hello. Everything works correctly in the online environment. Where do you run your code? Try recloning your strategy.

import qnt.data as qndata import qnt.stats as qns import qnt.graph as qngraph data = qndata.stocks.load_ndx_data(min_date="2005-01-01", assets=['NAS:GOOGL'])

12689361-ffcc-4ea5-b1b6-1c1704399adc-image.png

Dear @quani42,

Your submissions are in the queue and will be processed. Also, all submissions that are sent to the contest before the deadline will be eligible to take part in it.

Regards

@eddiee Hi, Mr. Eddie.

I am new to building strategies using ML/DL on Quantiacs and am very impressed with the OS performance of your ML strategies. I hope you can give me your contact (mail, limkedin,...) so I can learn from your experience in building an ML/DL strategy.

Sincerely thank.

@support
Actually, I've write the weights to the output function.
螢幕擷取畫面 2024-04-24 235034.png
I think the reason might be that the data was out-of-date when the strategy received at the weekend. After the data update in the next day, it failed to pass the test.

@newbiequant96 no problem.
I think the issue now is unrelated to the the previous issue. If you can show what is written above return code 1, I can maybe help.
It seems to be an issue in the code.
Regards

Dear @lookman,

Thank you for pointing at a problem with fundamental strategies. We are working on implementing a fix as soon as possible.

Regards

@buyers_are_back Hi,
the update was related to market_capitalization field availability and correctness, as vyacheslav_b described. Unfortunately, at the moment we cannot provide missing fundamental data, like number of ordinary shares, for some stocks (e.g. META). We are going to investigate potential new data sources in order to improve our datasets.
Regards,

@stefanm said in Collections has no attribute Iterable:

pip install progressbar2==3.55.0

Thank you so much @stefanm for support. Yes, it did work. Although on installation I did get these complaints:
qnt 0.0.306 requires tabulate>=0.9.0, but you have tabulate 0.8.10 which is incompatible.
qnt 0.0.306 requires xarray==0.20.2, but you have xarray 0.20.1 which is incompatible.

Also, my apologies for the late response. I had to put things aside for a day or so. Once again, thank you.

@machesterdragon Hello. Yes, it can never have too many examples 🙂

Inside the documentation sections, there are examples of strategies and technical indicators.
It's better to keep track of new strategy examples or updates on GitHub: https://github.com/quantiacs

@machesterdragon said in Cannot cast ufunc 'multiply' output from dtype('float64') to dtype('int64') with casting rule 'same_kind':

weights *= asset_filter

Hello. Change

weights *= asset_filter

to

weights = weights * asset_filter

You can see usage examples here:
https://quantiacs.com/documentation/en/user_guide/dynamic_assets_selection.html

Everything is working correctly now, try cloning the strategy.

@vg2001 Dear vg2001,
There were some problems with the servers, thank you for your patience.
Regards